| 题名 | A Survey on Deep Learning-Based Diffeomorphic Mapping |
| 作者 | |
| 通讯作者 | Tang, Xiaoying |
| 发表日期 | 2023
|
| ISBN | 9783030986605
; 9783030986612
|
| 来源专著 | |
| 出版地 | Berlin, Germany
|
| 出版者 | |
| 页码 | 1289-1321
|
| 摘要 | Diffeomorphic mapping is a specifific type of registration methods that can be used to align biomedical structures for subsequent analyses. Diffeomorphism not only provides a smooth transformation that is desirable between a pair of biomedical template and target structures but also offers a set of statistical metrics that can be used to quantify characteristics of the pair of structures of interest. However, traditional one-to-one numerical optimization is time-consuming, especially for 3D images of large volumes and 3D meshes of numerous vertices. To address this computationally expensive problem while still holding desirable properties, deep learning-based diffeomorphic mapping has been extensively explored, which learns a mapping function to perform registration in an end- to-end fashion with high computational effificiency on GPU. Learning-based approaches can be categorized into two types, namely, unsupervised and super- vised. In this chapter, recent progresses on these two major categories will be covered. We will review the general frameworks of diffeomorphic mapping as well as the loss functions, regularizations, and network architectures of deep learning-based diffeomorphic mapping. Specififically, unsupervised ones can be further subdivided into convolutional neural network (CNN)-based methods and variational autoencoder-based methods, according to the network architectures, the corresponding loss functions, as well as the optimization strategies, while supervised ones mostly employ CNN. After summarizing recent achievements and challenges, we will also provide an outlook of future directions to fully exploit deep learning-based diffeomorphic mapping and its potential roles in biomedical applications such as segmentation, detection, and diagnosis. |
| DOI | https://doi.org/10.1007/978-3-030-98661-2_108 |
| 学校署名 | 第一
; 通讯
|
| 来源库 | 人工提交
|
| 出版状态 | 正式出版
|
| 引用统计 |
被引频次[WOS]：0
|
| 成果类型 | 著作章节 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/527603 |
| 专题 | 南方科技大学 工学院_电子与电气工程系 |
| 作者单位 | 1.Southern University of Science and Technology ,Shenzhen, Guangdong, China 2.The University of British Columbia,Vancouver, BC, Canada |
| 第一作者单位 | 南方科技大学 |
| 通讯作者单位 | 南方科技大学 |
| 推荐引用方式 GB/T 7714 |
Yang, Huilin,Lyu, Junyan,Tam, Roger,et al. A Survey on Deep Learning-Based Diffeomorphic Mapping. Berlin, Germany:Springer International Publishing,2023:1289-1321.
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| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | 操作 | |
| 5.A Survey on Deep L（1350KB） | -- | -- | 限制开放 | -- | ||
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